Machine Learning market is growing exponentially – Statista forecasts this sector will reach $126B revenue by 2025. This cutting-edge technology already fuels global enterprises such as Amazon, Pinterest, and Netflix, bringing customized user experiences to their clients. Some other common applications of Machine Learning are automation in decision-making and enhancing of manufacturing processes. With all that in mind, companies might still hesitate before investing in the technology, not knowing if it is worth the impact. Here’s when Machine Learning consultants step in. After conducting thorough assessments, they will present you with the ML adoption strategy that will fit your business goals.
Planning to turn your idea of introducing ML into a success story? This article will unveil how you can do it with Machine Learning consulting services, what skills your tech consulting team members should possess, and how to deal with the most common challenges you might face in the process. Let’s dive in.
What is Machine Learning consulting?
ML consulting is aimed at helping enterprises boost their performance efficiency and orchestrate informed decision-making. In addition, data experts can smoothly introduce AI/ML algorithms to already existing solutions to boost user experience and improve product value.
Typically, ML consulting covers the following tasks:
- Gathering the team of specialists with relevant expertise and developing ML adoption strategies;
- Preparing the client’s data for integrating ML models;
- Implementing custom ML models and their integration into the client’s products.
According to Forbes, the five industries that utilize ML consulting and development services the most are manufacturing, housing construction, advertising, credit card security, and fintech. What are the main reasons enterprises reach out to ML consultants?
Related: Machine learning in finance: Why, what & how
What are the key advantages of Machine Learning consulting?
Partnering with Machine Learning consulting companies offers a wide range of benefits, from informed decision-making to improving operational efficiency. Let’s tap into the other less obvious advantages you get when buying into ML consulting:
Filling in your expertise gap with special skills, fast
As of February 2023, the number of job postings for “ML engineer” in the US reached 10K. The lack of qualified specialists on the market makes enterprises go through long hiring cycles, postponing the project launch. One of the greatest advantages of going for ML consulting services is that your tech vendor can define what expertise you need and provide you with niche specialists in the field of AI and ML. In addition, large tech companies with 1,000+ employees on board are more likely to have a considerable number of in-house data specialists, respectively. One of such companies is N-iX, where you could hire Machine Learning consultants and developers and ensure the project starter within days, saving your costs and time.
Evaluating the risks before investing in Machine Learning, objectively
In 2022, Forbes named AI and ML adoption an essential step for business growth. The McKinsey Global Survey stated that AI adoption has more than doubled since 2017 – half of the respondents implemented the technology in one or more business functions. However, with all the benefits, Machine Learning projects can also come with risks of overspending. You need to know your expenses and project timelines before you decide to invest in the technology.
The solution is to reach out to experienced Machine Learning consulting firms that can clearly define, qualify and prioritize your ML project objectives, build a roadmap and timelines, and accurately estimate project costs in advance. For instance, N-iX experts conduct rigorous Product Discovery that covers the ideation and analysis of the Machine Learning solution as well as the preparation of a detailed project plan.
Preparing your data, in advance
Thorough data preparation during the initial project stage is the key to trustworthy analytics in the long run. With a reliable Machine Learning consulting company by your side, you won’t have to worry that your ML solution will produce inaccurate results. Data specialists will help you identify the problem that will be your benchmark for data preparation, collect the data, and, finally, cleanse and structure it. As a result, your datasets will be in the right format for analysis, and you won’t face analytics errors and other data quality issues in the future.
Providing guidance throughout your ML journey, continuously
One of the most important advantages of ML consulting is the ability to discuss the common pitfalls and provide solutions on the go. Moreover, once you shake hands with ML consultants, you might be sure that your solution will undergo a thorough assessment at every implementation stage. Machine Learning consulting service list of tech vendors that follow the best quality control practices commonly includes ML solutions architecture reviews, data strategy reviews, as well as the roadmap and timelines assessments.
What characteristics define seasoned Machine Learning consultants?
As smart technologies are in greater demand than ever before, the number of ML specialists in prominent outsourcing destinations is growing. For example, Eastern Europe boasts a talent pool of more than 4,600 Machine Learning engineers. But how to choose the best specialists among the multitude of tech professionals? Let’s look closer at key aspects to consider when searching for qualified Machine Learning experts for hire:
Tech competencies
One of the main aspects to pay attention to when looking for Machine Learning consultants are their practical technical skills. Let’s take a look at the competencies top Machine Learning experts should possess:
- Solid knowledge of applied mathematics principles, including probability theory, linear algebra, etc.;
- Hands-on experience with neural networks;
- Excellent command of data modeling, evaluation, and predictive analytics;
- Deep knowledge of advanced signal processing techniques;
- Profound expertise in natural language processing (NLP);
- Thorough understanding of audio and video processing principles.
Qualification level
Your project’s outcome directly depends on the average seniority level of your Machine Learning experts. Data specialists with 4+ years of experience and a proven track record of successful projects are more likely to solve complex tech challenges and pay more attention to quality control. So, to ensure streamlined development processes during the project, hire Machine Learning consultants of Middle and Senior qualification levels and a vast portfolio.
Here, at N-iX, the average Middle specialist in the field of Data Science has at least four years of experience, while Senior specialists – over 5 years.
Professional certifications
Another thing that adds extra credit to specialists in the ML consulting services market are professional recognitions. They confirm that the person not only possesses basic practical Machine Learning skills, for instance, deep learning but also can deal with specialized ML areas such as time series analysis and survival analysis. IBM Machine Learning Professional Certificate, AWS Certified Machine Learning, and Google Professional Machine Learning Engineer Certification are among the most popular ML certifications.
What are the challenges of Machine Learning consulting, and how to overcome them?
However, sometimes ML consulting is not without its challenges. Let’s look closer at the most common pitfalls and possible solutions:
Poor data quality
One of the key challenges associated with Machine Learning adoption is poor data quality. Data inconsistencies and gaps might lead to the ill-performance of your ML solution. On the other hand, many enterprises have to deal with another issue – they gather a large amount of irrelevant data that stands in the way of accurate analytics.
Solution: To minimize challenges related to poor data quality, your Machine Learning consulting company should make efforts to optimize the data collection process. Our Big Data experts at N-iX, for example, are well-versed in data cleaning, labeling, and warehousing and will effectively organize your data sets before implementing ML algorithms.
Storage needs
Implementation of Machine Learning models requires the processing of large volumes of data, which directly affects the IT infrastructure requirements. Enterprises that rely on traditional block-based storage technologies have to deal with space limitations and high infrastructure expenses.
Solution: Machine Learning consulting firms are able to resolve this bottleneck by introducing alternative methods of data storage, such as cloud infrastructure. Here, at N-iX, we can help you streamline data and analytics processes by migrating your existing database to the cloud or developing a cloud-based data infrastructure from scratch.
Keep reading: Scaling your infrastructure in the cloud: How to handle huge traffic spikes
Low maturity level of ML infrastructure
Hiring Machine Learning experts that can build ML models is just the first step. One of the key issues is that the implementation and deployment of these models is entirely manual. In addition, manual Machine Learning models operate without CI/CD pipelines, so when they are deployed in the real world, they often malfunction.
Solution: Consider Machine Learning consulting companies with proven experience in ML model automation as well as the implementation of CI/CD for Machine Learning solutions. For example, N-iX has cooperated with a global Fortune 100 manufacturing enterprise to implement CV algorithms and an automated ML system for streamlining the client’s inventory management. The project scope also spanned the introducing of Continuous Delivery for ML, which allowed to implement the continuously repeatable cycles of testing and deployment of Machine Learning models.
Related: Machine Learning and marketing automation: How to make it work
Why should you choose N-iX Machine Learning experts to gain the maximum value out of your data?
- N-iX is a global software engineering company with 21 years of proven experience in tech consulting and custom software development for manufacturing, logistics and supply chain, fintech, telecom, and other industries;
- Our data team consists of 200 specialists with expertise in building AI strategies, ML models implementation, deploying Machine Learning pipelines, model governance, and other services;
- N-iX client list spans long-term relationships with Gogo, AVL, Lebara, cleverbridge, Ringier, and many Fortune 500 companies;
- We have a vast portfolio of ML-related projects that includes the development of Computer Vision solutions, implementation of NLP models, advanced search platforms, and neural networks;
- We comply with international data protection standards, including ISO 27001:2013, ISO 9001:2015, GDPR, and PCI DSS;
- N-iX is well-recognized in the industry: the vendor has reached top positions in the IAOP ranking several years in a row and appeared on CRN and GSA UK lists.
